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Full-Text Articles in Business
A Study Of The Impact Of Data Intelligence On Software Delivery Performance, Yongdong Dong
A Study Of The Impact Of Data Intelligence On Software Delivery Performance, Yongdong Dong
Dissertations and Theses Collection (Open Access)
With the rise of big data and artificial intelligence, data intelligence has gradually become the focus of academia and industry. Data intelligence has two obvious characteristics: big data drive and application scene drive. More and more enterprises extract valuable patterns contained in data with prediction and decision analysis methods and technologies such as large-scale data mining, machine learning and deep learning and use them to improve the management and decision in complex practice, so as to promote changes of new business modes, organizational structures and even business strategies, and improve the operational efficiency of organizations. However, there are few studies …
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander
School of Business: Faculty Publications and Other Works
Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …
How To Derive Causal Insights For Digital Commerce In China? A Research Commentary On Computational Social Science Methods, David C.W. Phang, Kanliang Wang, Qiu-Hong Wang, Robert John Kauffman, Maurizio Naldi
How To Derive Causal Insights For Digital Commerce In China? A Research Commentary On Computational Social Science Methods, David C.W. Phang, Kanliang Wang, Qiu-Hong Wang, Robert John Kauffman, Maurizio Naldi
Research Collection School Of Computing and Information Systems
The transformation of empirical research due to the arrival of big data analytics and data science, as well as the new availability of methods that emphasize causal inference, are moving forward at full speed. In this Research Commentary, we examine the extent to which this has the potential to influence how e-commerce research is conducted. China offers the ultimate in data-at-scale settings, and the construction of real-world natural experiments. Chinese e-commerce includes some of the largest firms involved in e-commerce, mobile commerce, social media and social networks. This article was written to encourage young faculty and doctoral students to engage …
Analytic Extensions To The Data Model For Management Analytics And Decision Support In The Big Data Environment, Nsikak Etim Akpakpan
Analytic Extensions To The Data Model For Management Analytics And Decision Support In The Big Data Environment, Nsikak Etim Akpakpan
Walden Dissertations and Doctoral Studies
From 2006 to 2016, an estimated average of 50% of big data analytics and decision support projects failed to deliver acceptable and actionable outputs to business users. The resulting management inefficiency came with high cost, and wasted investments estimated at $2.7 trillion in 2016 for companies in the United States. The purpose of this quantitative descriptive study was to examine the data model of a typical data analytics project in a big data environment for opportunities to improve the information created for management problem-solving. The research questions focused on finding artifacts within enterprise data to model key business scenarios for …
Vungle Inc. Improves Monetization Using Big-Data Analytics, Bert De Reyck, Ioannis Fragkos, Yael Gruksha-Cockayne, Casey Lichtendahl, Hammond Guerin, Andre Kritzer
Vungle Inc. Improves Monetization Using Big-Data Analytics, Bert De Reyck, Ioannis Fragkos, Yael Gruksha-Cockayne, Casey Lichtendahl, Hammond Guerin, Andre Kritzer
Research Collection Lee Kong Chian School Of Business
The advent of big data has created opportunities for firms to customize their products and services to unprecedented levels of granularity. Using big data to personalize an offering in real time, however, remains a major challenge. In the mobile advertising industry, once a customer enters the network, an ad-serving decision must be made in a matter of milliseconds. In this work, we describe the design and implementation of an ad-serving algorithm that incorporates machine-learning methods to make personalized ad-serving decisions within milliseconds. We developed this algorithm for Vungle Inc., one of the largest global mobile ad networks. Our approach also …
Semantic Inference On Clinical Documents: Combining Machine Learning Algorithms With An Inference Engine For Effective Clinical Diagnosis And Treatment, Shuo Yang, Ran Wei, Jingzhi Guo, Lida Xu
Semantic Inference On Clinical Documents: Combining Machine Learning Algorithms With An Inference Engine For Effective Clinical Diagnosis And Treatment, Shuo Yang, Ran Wei, Jingzhi Guo, Lida Xu
Information Technology & Decision Sciences Faculty Publications
Clinical practice calls for reliable diagnosis and optimized treatment. However, human errors in health care remain a severe issue even in industrialized countries. The application of clinical decision support systems (CDSS) casts light on this problem. However, given the great improvement in CDSS over the past several years, challenges to their wide-scale application are still present, including: 1) decision making of CDSS is complicated by the complexity of the data regarding human physiology and pathology, which could render the whole process more time-consuming by loading big data related to patients; and 2) information incompatibility among different health information systems (HIS) …
Proactive It Incident Prevention: Using Data Analytics To Reduce Service Interruptions, Mark G. Malley
Proactive It Incident Prevention: Using Data Analytics To Reduce Service Interruptions, Mark G. Malley
Walden Dissertations and Doctoral Studies
The cost of resolving user requests for IT assistance rises annually. Researchers have demonstrated that data warehouse analytic techniques can improve service, but they have not established the benefit of using global organizational data to reduce reported IT incidents. The purpose of this quantitative, quasi-experimental study was to examine the extent to which IT staff use of organizational knowledge generated from data warehouse analytical measures reduces the number of IT incidents over a 30-day period, as reported by global users of IT within an international pharmaceutical company headquartered in Germany. Organizational learning theory was used to approach the theorized relationship …
Era Of Big Data: Danger Of Descrimination, Andra Gumbus, Frances Grodzinsky
Era Of Big Data: Danger Of Descrimination, Andra Gumbus, Frances Grodzinsky
WCBT Faculty Publications
We live in a world of data collection where organizations and marketers know our income, our credit rating and history, our love life, race, ethnicity, religion, interests, travel history and plans, hobbies, health concerns, spending habits and millions of other data points about our private lives. This data, mined for our behaviors, habits, likes and dislikes, is referred to as the “creep factor” of big data [1]. It is estimated that data generated worldwide will be 1.3 zettabytes (ZB) by 2016. The rise of computational power plus cheaper and faster devices to capture, collect, store and process data, translates into …
The Use Of Business Intelligence Techniques In Supply Chain Performance, Jue Gu
The Use Of Business Intelligence Techniques In Supply Chain Performance, Jue Gu
Open Access Theses
Who likes data? Businesses are always loyal data followers. Companies analyze various forms of data to maintain businesses and identify their current performance in different areas so they can find business opportunities to improve and obtain more market share in advance (Qrunfleh & Tarafdar, 2012). When Big Data comes to businesses, companies who can take advantage of data the best tend to regularly get more business and customers (Waller & Fawcett, 2013). Collecting, analyzing, and demonstrating data could be essential to a single business, a company's supply chain performance and its sustainability. As an intelligent data processing product in terms …